Amphibious Entrepreneurs and the Emergence of Organizational Forms*

1. Amphibious Entrepreneursand the Emergence of Organizational Forms* Walter W. Powell Kurt Sandholtz Stanford University 2012 This work appears in two formats, as chapter 13 in The Emergence of Organizations and Markets, J. Padgett and W. Powell, Princeton University Press, 2012, and in a different form in Strategic Entrepreneurship Journal, in press.

2. Motivating questions: • What fosters the emergence of and variety among organizational forms? Form: the set of characteristics that identify an organization as both a unique entity and a member in a group of like entities (Romanelli 1991) • In what ways might a pragmatistaccount of entrepreneurshipchallenge and/or complement prevailing perspectives? Put differently, what arguments are less heroic and instrumental, more boundedlyagentic and improvisational, and more theoretically compelling? If we avoid sampling on the dependent variable (looking only at success stories), can we discern which elements combine, in novel ways, to produce “fresh action”?

3. Mechanism of novelty #1: Recombination • Innovation is an interstitial phenomenon • Tools, concepts, and practices from one domain are combined with those of a proximal domain • Reassembly of known elements generates many technological and organizational innovations • Ample theoretical and empirical support (Arthur 2009, Nelson and Winter 1982, Schumpeter 1942)

6. Mechanism of novelty #2: Transposition • Transposition creates new interstices • Tools, concepts, and practices from one domain are introduced into settings where they are foreign • The assembly of previously unrelated practices can produce social invention • Less frequent, and much less likely to be successful • But even failures at transposition can generate experiments that have profound tipping effects

10. Amphibious entrepreneurs • Simultaneously occupy positions of influence in two distinct domains • Act as agents of transposition, carrying practices, assumptions, and decision premises across domains • As such, often seen as “trespassers” or “rule creators” (Becker 1963) • not boundary-spanners doing import/export • not “strategic actors” engaging in arbitrage • Play a crucial, albeit unintentional role in the emergence of novel forms

11. A pragmatist view of entrepreneurship • When established routines prove lacking, people search and experiment (Dewey, 1938; Becker, 1986; Stark, 2009) • People have little choice, however, but to draw on their stock of existing knowledge to cope with situations without precedent • Existing knowledge and routines in new settings offer the possibility of novel social arrangements

12. Empirical setting: the invention of a new model of organizing - - the DBF • The “dedicated biotech firm” (DBF) emerged in the early ‘70s • Distinct from corporate hierarchies, universities, and government labs, but with practices transposed from each: • fundamental scientific research • horizontal structure of information flow • project-based organization of work • porous organizational boundaries • strong protection of intellectual capital • unprecedented venture financing (quantity and duration)

13. Fertile ground for studying emergence of new organizational forms “It was like maybe a dam waiting to burst or an egg waiting to hatch, but the fact is, there were a lot of Nobel Prizes in molecular biology, but no practical applications.” -- Ron Cape, Cetus co-founder

14. Political and economic conditions complemented scientific advances • Massive political support for university-industry tech transfer, most notably Bayh-Dole Act passed in 1980 • Diamond v. Chakrabarty (1980) Supreme Court decision permitted patenting of man-made living organisms • ERISA and “Prudent Man” rulings permitted pensions and endowments to be invested in high-risk VC funds • But poisedness does not imply predictability, nor dictate potential outcomes

15. No evidence of a biotech blueprint borrowed from ICT or physical sciences “We were naïve. I think if we had known everything about all the potential huge competitors, we might not have even done it. One of the benefits we had, I suppose, was some combination of naïveté and ambition and this desire to do something on our own. I think there was a feeling of a green field, and that we were the first…We did not have the business model mapped out, or the ultimate value proposition, which are all things that we do today in doing a startup.” -- Brook Byers, VC & 1st CEO of Hybritech

16. Why we chose to study the first decade • 1972 provides a natural starting point • Seminal papers on rDNA presented at conferences • First bioscience firm founded: Cetus • By 1981, legal and political foundation was in place • After 1982, serial entrepreneurs began founding second biotech ventures (replication of early models) • Limits of archival record: pioneers attract more attention, easier to find contemporary accounts of their founding 16

17. Table 1: DBFs founded in the first 10 years

18. Method: Multi-case comparison • Reliance on accounts made in the 1970s and ‘80s by the founders (in newspapers, magazines, TV interviews, annual reports, IPO prospectuses, etc.) • 2,000 plus pages of oral histories in UC Berkeley Bancroft Library collection • Excellent science journalism and scholarship chronicling the era (Kenney 1986; Hall 1987; Teitelman 1989; Wright 1994; Robbins-Roth 2000; Vettel 2006) • Supplemented by our own interviews with founders, board members, and VCs

19. Table 2: Summary of data sources

20. Sequence of analysis Developed detailed case histories of each company’s founding Distilled salient attributes and practices within each case Cross-case comparison yielded 28 unique DBF practices; consolidated and winnowed to 13 practices that were shared by at least five of the firms Coded all companies for the presence/absence (1/0) of these practices

21. Attributes present in more than half the companies

22. Attributes present in more than a third of the DBFs

23. Attributes present in five or more of the DBFs

25. Hierarchical cluster analysis (HCA) • Multivariate technique originally used to create phylogenetic trees from taxonomic data; subsequent uses range from medical image analysis to market research • Useful for samples where 8 < n < 100 (“tweeners”) • Accommodates both a rich reconstruction of each firm’s founding story and a rigorous cross-case analysis of how practices cohered • Why not QCA? • Binary coding allows crisp-set analysis; “fuzzy logic” QCA not necessary • QCA most useful for determining multiple pathways to outcomes; our focus is less on outcomes and more on processes by which practices were combined • Deep knowledge of the cases both precedes and follows HCA in the sequence of our analysis

26. How we used HCA • Input: rectangular matrix of 26 firms x 13 practices • Intermediate step: square matrix of mathematical dissimilarity between each pair of biotech firms • Output: • “Textual dendrogram” showing how clusters of firms begin to cohere around common sets of practices • Tree diagram graphically depicting the clusters • Measures of cluster adequacy to help determine “where to cut the tree” (i.e., optimal level of homogeneity within and heterogeneity between clusters)

27. Textual dendrogram (aka “icicle diagram”) We selected four clusters as the optimal level of agglomeration

28. Figure 3: Selecting the optimal number of clusters “Elbow” suggests optimal number of clusters. At < 4 clusters, all firms rapidly lump together. Beyond 4 clusters, the degree of internal dissimilarity decreases much more slowly. (# of attributes shared with firms outside the cluster – # of attributes shared with firms within the cluster) E-I ratio = # total shared attributes (Krackhardt and Stern, 1988)

29. Branches of the DBF Tree ImmunoGen IntegratedGenetics Cetus DNAX Genex Cytogen Hybritech Biogen Genentech GeneticsInstitute ZymoGenetics MolecularGenetics Amgen Chiron Centocor Genzyme Repligen SIBIA Immunex Xoma Codon Synergen CaliforniaBiotech EnzoBiochem GeneticSystems Seragen 2 4 3 1 The Dedicated Biotech Firm

30. “In business to do science” “In science to do business”

31. Four DBF Clusters “One reason I called this company Integrated Genetics , instead of something else, was because I wanted a company with the integrated functions of research, development, sales and marketing, and not just R & D." -- David Housman, Integrated Genetics co-founder and professor of biology, MIT, Boston Globe, Dec 20, 1983 Centocor’s strategy was to be “the bridge from the academic research laboratory to the established health care supplier” (Centocor 1982 Annual Report) “We realized it was a lot cheaper to roam academe and pay a royalty back for what we developed than start our own research facilities.” (Founding CEO Hubert Schoemaker) “Much of Amgen’s success in raising capital can be attributed to the fact that every one of our senior managers had worked for large corporations. As a result, we had the organizational discipline of a far bigger company, with salary grades, annual performance reviews, monthly reports, and budgets that were taken seriously. All the things that the start-ups rarely do, we did; to us, it was second nature.” – Gordon Binder, Amgen’s first CFO and second CEO “According to a study just completed by the Philadelphia-based Institute for Scientific Information (ISI), Genentech leads the biotechnology industry for the period 1981 through June of 1992 in all three categories measured: greatest number of publications, greatest number of citations, and greatest number of citations per paper. . . . Genentech also achieved a very high comparative ranking in citations per paper when compared to five of America's best university departments of biological sciences. Genentech was second only to the Massachusetts Institute of Technology's (MIT) Department of Biology of the five schools evaluated” (UCSF, Stanford, UC-Berkeley, and Princeton). -- Genentech press release Oct. 23, 1992 Amphibious scientific founder? N Y Just-off-campuslocation? Y N Engaged in contractresearch? Y N • Cluster 1 • Differentiating attributes: • Amphibious scientific founders • Emphasized publishing scientific results • Not reliant on SAB for research direction • Biogen, California Biotech, • Cetus, Chiron, DNAX, • Genentech, Genetics Institute, Immunex,Molecular Genetics, • Repligen, Seragen, • Synergen , ZymoGenetics • Cluster 2 • Differentiating attributes: • VC in operational role • Senior pharma exec. recruited as CEO • Noted scientists involved as founders or on advisory board, but publishing was not emphasized • Resembled spin-offs from academic labs • Genzyme, Hybritech, • ImmunoGen, Integrated Genetics, SIBIA, Xoma • Cluster 3 • Differentiating attributes: • Focused on diagnostics and other non- therapeutic applications • Few research contracts with large corporations (i.e., “little r, big D”) • Scientific breakthroughs in-licensed from academy • Centocor, Codon, Genetic Systems • Cluster 4 • Differentiating attributes: • Deliberately assembled business venture • Repeat entrepreneur among founders • Pursued growth by acquisition • Located away from campus • Amgen, Cytogen, Genex, • Enzo

32. Four DBF Clusters Amphibious scientific founder? N Y Just-off-campuslocation? Y N Engaged in contractresearch? Y N • Cluster 1 • Differentiating attributes: • Amphibious scientific founders • Emphasized publishing scientific results • Not reliant on SAB for research direction • Biogen, California Biotech, • Cetus, Chiron, DNAX, • Genentech, Genetics Institute, Immunex,Molecular Genetics, • Repligen, Seragen, • Synergen , ZymoGenetics • Cluster 2a • Differentiating attributes: • VC in operational role • Senior pharma exec. recruited as CEO • Noted scientists involved as founders or on advisory board, but publishing was not emphasized • Resembled spin-offs from academic labs • Genzyme, Hybritech, • ImmunoGen, Integrated Genetics, SIBIA, Xoma • Cluster 2c • Differentiating attributes: • Deliberately assembled business venture • Repeat entrepreneur among founders • Pursued growth by acquisition • Located away from campus • Amgen, Cytogen, Genex, • Enzo • Cluster 2b • Differentiating attributes: • Focused on diagnostics and other non- therapeutic applications • Few research contracts with large corporations (i.e., “little r, big D”) • Scientific breakthroughs in-licensed from academy • Centocor, Codon, Genetic Systems

33. Publication quantity and quality by cluster* • Cluster 1 • Average publications per company • 584.54 • Average citations per publication • 66.63 • Biogen, California Biotech, • Cetus, Chiron, DNAX, • Genentech, Genetics Institute, Immunex,Molecular Genetics, • Repligen, Seragen, • Synergen , ZymoGenetics • Cluster 2 • Average publications per company • 185.83 • Average citations per publication • 29.12 • Genzyme, Hybritech, • ImmunoGen, Integrated Genetics, SIBIA, Xoma • Cluster 4 • Average publications per company • 266.25 • Average citations per publication • 44.76 • Amgen, Cytogen, Genex, • Enzo Cluster 3 Average publications per company 148.67 Average citations per publication 45.35 Centocor, Codon, Genetic Systems * Publications tracked for 1st 10 years post-IPO. Citations as of Oct. 2010, self-cites excluded. Self cites disproportionately boost Cluster 1’s citation counts. Source: ISI Web of Science

34. Consequences (in a narrow sense) • Three recombinatorial DBF variants mixed and matched practices borrowed from past experience • One DBF variant was associated with amphibians who naively imported practices of the invisible college into venture-financed startups • Trespassing was the mother of invention: new scientific norms and new models of funding improvised on the fly • Similar financial events, very different meanings: • Acquisition by big pharma – security for recombination-based firms vs. “end of Camelot” for transposition-based firms • IPO – liquidity event vs. “currency exchange” (scientific papers converted into investment capital; helped retain junior scientists). • Publications – scientific leadership vs. “giving away crown jewels”

35. Impact of the DBF organizing models • Scientific productivity of firms that were “in business to do science” catalyzed changes in the conservative halls of the academy • Commercial success of firms that were “in science to do business” has resulted in a reordering of drug discovery in the pharmaceutical industry • Result: blurred boundaries between university and commercial science “The life sciences innovation system has ultimately replacedthe traditional divide between university science and pharmaceutical innovation with a system that depends on interdependent and collaborative knowledge development spanning both public and private organizations.” (Cockburn and Stern 2010)

36. Consequences (a broader view) • Recombination and transposition can both give birth to new organizational models • Recombinatorial novelty is an interstitial phenomenon (Edelman et al., 2001; Morrill 2008) • Transposition represents the creation of new interstices, freighted with generative potential • Practices flowing across newly-created interstices catalyzed changes in the conservative halls of the academy and industry, having effects well beyond these organizations, opening up previously unconsidered possibilities in different domains. • A relational view of entrepreneurship - - amphibians as unintended enablers of social invention; novelty as a consequence of traffic across social worlds, not individual creativity or agency.

37. Feedback dynamics transform the academy and industry Academy: • Embrace and celebration of academic entrepreneurship; remaking of departments and schools to focus on translational research; adoption of metrics to evince innovativeness; industry jobs no longer frowned on, indeed encouraged. Industry: • Demise of insular internal R&D labs in Big Pharma; much greater dependence on external sources of knowledge; creation of corporate nonprofit institutes to do collaborative work; funding of postdocs; encourage publishing • Campus-like settings to attract the creative class • Entrepreneur-in-residence programs at venture capital firms Both: • From discipline and department to projects • Not a settlement but a continuing disruption, most notably in careers and rewards Not surprisingly, recombination proved a more robust business model in the short term, but transposition had much more far-reaching long-term consequences.

38. Implications • In the short run, actors make relations. This is a story of pragmatic search, where the tools of everyday practice were used in unfamiliar circumstances, at a time when there was a green field. • In the long run, relations make actors. In those settings where science was repurposed, the tools and new interactions concatenated to form new entities with effects that extended far beyond their initial intentions. • Some tools are more malleable than others; some regimes of worth allow more ambiguity; some solutions to problems are less specific to particular contexts. The principles and practices of open science both enroll and mediate, undercutting some of the hierarchy of the corporate world, and challenging some of the privileges formerly reserved for the academic priesthood.